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RUNLOCALAI · v38
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  6. /Ch. 1
Capstone: Research AI System

01. Capstone Overview

Chapter 1 of 18 · 10 min
KEY INSIGHT

A successful capstone project requires aligning novelty, rigor, and reproducibility from day one—not as afterthoughts. This course guides you through building a complete research AI system: from initial idea to published artifact. Unlike tutorial-based courses, the capstone demands genuine contribution—something that advances the state of knowledge in your chosen area. The project lifecycle has four phases: 1. **Formulation** (Chapters 1-3): Define the research question, survey related work, and establish your contribution's novelty. 2. **Construction** (Chapters 4-5): Design the architecture and implement the system. 3. **Evaluation** (Chapters 6-9): Select baselines, design experiments, and analyze results. 4. **Communication** (Chapters 10-18): Write the paper, release code, and prepare for replication. **Novelty vs. Increment:** Your contribution need not be approach-shifting. Modifying a transformer attention mechanism with a new positional encoding scheme counts as valid novelty. What matters is that the change is intentional, justified by theory or observation, and empirically validated. **Common Pitfall:** Many operators spend months building an impressive system only to realize they cannot properly evaluate it because no established baselines exist for their task. Chapter 6 addresses baseline selection before implementation begins. **Deliverable:** By the end of this course, you will produce: - A novel architecture with documented design decisions - An open-source implementation - An ablation study isolating contribution components - A quantitative evaluation against strong baselines - A qualitative analysis explaining behavioral differences - A camera-ready paper draft

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

EXERCISE

Write a one-paragraph project proposal identifying your target task, the expected improvement over baselines, and the evaluation metric you will use. This proposal will evolve through subsequent chapters.

← Overview
Capstone: Research AI System
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Research Question